Philadelphia Mobility Patterns in 2020
A report on how movement is during the pandemic
Andrew Renninger, MCP (@AndrewRenninger) and Ken Steif, PhD (@KenSteif)
1 Introduction
As the coronavirus spread in Philadelphia this year, travel patterns changed. With this shock to human mobility came another to business activity, as far fewer of us traveled to work, amenities or other pastimes. In order to understand the consequences of these changes, we use GPS data from mobile phones explore how Philadelphians changed their travel patterns before and during the pandemic.
With the goal of understanding the time-space patterns of resident movement in Philadelphia, the following section presents data from SafeGraph, a provider of mobility data collected from mobile devices. Note that SafeGraph collects data on a representative sample (10%) of the population across the country, so our indicators are not the true number of visits or journeys, but a slice. The data contain the terms defined in Figure 1.1: the number of visitors is the count of devices flowing to a point of interest (POI) while a connection is an origin-destination line between a Census Block Group and a point of interest, regardless of its weight. A flow is also a connection, but with a weight measuring the number of visitors traveling between origin and destination.
1.1 Key definitions
Each visit is a mobile device entering into a point of interest; these include parks and museums, restaurants and bars, or offices and hospitals. In Figure 1.2 we map the distribution of these venues and businesses for context. We classify each point of interest by its description, which SafeGraph provides. 1 We can see that most businesses cluster in Center City or nearby but no businesses cluster more than restaurants and bars.
1.2 Distribution of points of interest
To demonstrate how this information combines to form a network, Figure 1.3 connects origins to destinations by month in Philadelphia. This network is what we will be exploring below.
1.3 Connections over time
This analysis comprises different spatial scales, Citywide, Neighborhood and Point of Interest. We can look globally, across the city, to explore trends throughout; we can also think locally, dividing the city up into cells or neighborhoods to probe variations within the city. Finally, we can look at individual businesses or venues. Below, we attempt to understand patterns at each scale.
2 Citywide analysis
In this section we explore trends and relationships manifest most strongly citywide. To see how the business environment is for chains across the city, rather than any given location, we sum visits by brand. Figure 2.1 shows that brands deemed essential businesses saw comparably less of a decline than others, along with fast food restaurants. The map shows the locations of brands for context.
2.1 Relative brand performance
| Rankings | Locations |
|---|---|
 |
 |
Figure 2.2 ranks each brand by the number of visitors it received and animates this change through the pandemic. Dollar stores rise gradually throughout the year; another important shift is away from non-essential retail towards essential businesses like pharmacies. Starbucks and Wawa occupy top spots for the first several weeks of the year but when the shelter-in-place order sets in, visits collapse and they are replaced in the ranks by essentials RiteAid and ShopRite.
2.2 Comparing traffic by brand
In Figure 2.1 we aggregate by use, grouping by classes like leisure (restaurants and bars) and tourism (museums and theaters). The pandemic had curbed visits to each class of business, but particularly leisure and other, which includes office spaces. Interestingly, tourism is recovering while shopping and grocers are not, perhaps as many switch to digital commerce.
2.3 Industry trends by category
Changing mobility may be exasperating issues relating to equity. Philadelphia still shows patterns of concentrated poverty, segregated housing and isolated pockets of prosperty; the pandemic could produce deeper disparities. One risk is that communities of color and low income neighborhoods will not be able to socially distance in the same capacity as affluent communities. The evidence for this is mixed. The charts below plot change in outflows from a Census Tracts as a function of both income and race, respectively. The vertical axis shows the change in travel from a January/February baseline, so a greater reduction indicates stronger adherance to social distancing rules. During the critical month of April few poor communities could afford to shelter in place at the rate that wealthy ones could.
2.4 Did trips change with income?
In March, at the onset of the pandemic, more Philadelphians were leaving their home tracts. In April, all communties see reductions in trips. However, the higher the median income, the greater the reduction in trips. The reverse is true for race: the higher the minority population, the greater the reduction of trips, but the relationship diminishes during the summer.
2.5 Did trips change with race?
Next we look at how mobilities varies across neighborhoods and commercial corridors to determine whether or not pandemic is impacting some parts of the city more than others.
3 Neighborhood variation
In this section, we present trends across neighborhoods and other smaller geographies. These allow us to see how visits in particular are changing in different parts of Philadelphia. We find large disparities between the best and worst performing regions.
We explore trends across neighborhoods in Figure 3.1; neighborhoods dominated by office work, like the Navy Yard along with Logan Square and Center City, saw precipitous declines in foot traffic, but those with strong amenities and residential communities have recovered. This suggests that demand for food, drink, and shopping may be shifting away from the core. (Note: see the appendix for larger tables.)
3.1 Neighborhood trends
| Best | Worst |
|---|---|
 |
 |
In order to understand trends we fit a rolling average to these neighborhoods and plot these trends for context. The many neighborhoods in the Northeast and Northwest are rebound while the axis of University, Center and Old City are still down substantially.
3.2 Rolling averages
Neighborhoods provide meaningful boundaries at a lower resolution, but dynamics are evident at smaller scales as well. Figure 3.2 shows visits by 500 by 500m grid cells. We are still aggregating from points of interest, so this is visits to businesses, parks, museums and the like, but by tile; this does not include visits to the particular patch of land without setting foot in a point of interest. The city hollowed out during the worst months of the pandemic but the Old City, Center City, University City axis still appears to have pockets of thriving activity in these maps.
3.3 Visits by month in gridded units
Businesses are not evenly distributed across the city, however, so understanding business activity requires a unit of analysis that respects commercial corridors—zones where businesses cluster together—of which the city has designated roughly 280. We look at restaurants and bars as an indicator of night life in Figure 3.3. The largest are Market West and Market East, on either side of city hall, with 1712 and 1263 restaurants and bars respectively, following by Old City at 654 and another in University City with 493: most of the business activity is concentrated in a few locales.
3.4 Commercial corridors
Plotting trends in these corridors over time, the greatest reduction in visitors is in Center City and at the Sports Complex. Plazas like Oxford and Levick, home to a supermarket, and City and Haverford see the smallest impact.
3.5 Corridor trends
| Best | Worst |
|---|---|
 |
 |
4 Points of Interest in focus
This section looks at individual points of interest, how they perform over time and whether or not we can identify certain bellwether businesses. These are specific cases that can provide further insight into how the pandemic is changing mobility. Breaking out the above animation, we can see that the drop in mobility primarily impacts businesses at the center while clusters along the periphery maintain connections.